R: Kernel Weights

kweights {sandwich} | R Documentation

## Kernel Weights

### Description

Kernel weights for kernel-based heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimators as introduced by Andrews (1991).

### Usage

    kweights(x, kernel = c("Truncated", "Bartlett", "Parzen",
      "Tukey-Hanning", "Quadratic Spectral"), normalize = FALSE)

### Arguments

`x` | numeric.
`kernel` | a character specifying the kernel used. All kernels used are described in Andrews (1991).
`normalize` | logical. If set to `TRUE` the kernels are normalized as described in Andrews (1991).

### Value

Value of the kernel function at `x`.

### References

Andrews DWK (1991), Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation. Econometrica, 59, 817–858.

### See Also

`kernHAC`, `weightsAndrews`

### Examples

    curve(kweights(x, kernel = "Quadratic", normalize = TRUE),
          from = 0, to = 3.2, xlab = "x", ylab = "k(x)")
    curve(kweights(x, kernel = "Bartlett", normalize = TRUE),
          from = 0, to = 3.2, col = 2, add = TRUE)
    curve(kweights(x, kernel = "Parzen", normalize = TRUE),
          from = 0, to = 3.2, col = 3, add = TRUE)
    curve(kweights(x, kernel = "Tukey", normalize = TRUE),
          from = 0, to = 3.2, col = 4, add = TRUE)
    curve(kweights(x, kernel = "Truncated", normalize = TRUE),
          from = 0, to = 3.2, col = 5, add = TRUE)

[Package sandwich version 2.3-4 Index]